Elasticsearch vs Sphinx

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Elasticsearch vs Sphinx: What are the differences?

Introduction

Elasticsearch and Sphinx are both search platforms used for indexing and querying large sets of data. While they serve similar purposes, there are several key differences between them. Below are the key differences between Elasticsearch and Sphinx.

  1. Scalability: Elasticsearch is highly scalable, allowing you to distribute and replicate data across multiple shards and nodes. It can handle massive amounts of data and scale horizontally by adding more nodes to the cluster. On the other hand, Sphinx is not designed for horizontal scalability. It relies on a single server and cannot distribute data across multiple nodes. Therefore, Elasticsearch is a better choice for applications requiring high scalability.

  2. Full-text search capabilities: Elasticsearch is known for its powerful full-text search capabilities. It analyzes text during indexing, allowing for efficient searching and relevance scoring. It supports advanced features like stemming, synonym expansion, and language-specific analyzers. Sphinx, although it does support full-text search, lacks some of the advanced features provided by Elasticsearch. It is more suitable for simpler search requirements.

  3. Real-time data: Elasticsearch is designed for near real-time data retrieval and analysis. It provides low latency indexing, making it suitable for applications that require real-time insights. Sphinx, on the other hand, is primarily designed for batch processing and does not provide real-time indexing and querying capabilities. If you need immediate access to fresh data, Elasticsearch is the better choice.

  4. Querying capabilities: Elasticsearch supports a wide range of queries, including text search, term and range queries, bool queries, and more. It also offers powerful aggregations and filtering options for complex data analysis. Sphinx, while it does provide various querying options, has a more limited query language compared to Elasticsearch. If you require complex querying and analysis capabilities, Elasticsearch is the more suitable choice.

  5. Community and ecosystem: Elasticsearch has a large and active community, which results in a rich ecosystem of plugins, integrations, and support. There is a wide range of documentation and resources available for learning and troubleshooting. Sphinx also has a community, but it is comparatively smaller and less active than Elasticsearch's community. The larger community and ecosystem of Elasticsearch make it easier to find resources and get support.

  6. Data sources: Elasticsearch can index data from various sources, including JSON, XML, relational databases, and more. It provides built-in connectors for popular databases and supports easy integration with different data sources. Sphinx, on the other hand, primarily focuses on indexing data from SQL databases. If you have diverse data sources beyond SQL databases, Elasticsearch provides more flexibility.

In summary, Elasticsearch is highly scalable, offers advanced full-text search capabilities, supports real-time data retrieval, provides powerful querying options, has a larger community and ecosystem, and can index data from diverse sources. Sphinx, on the other hand, lacks scalability, advanced search features, real-time capabilities, comprehensive querying options, and a robust community.

Advice on Elasticsearch and Sphinx
André Ribeiro
at Federal University of Rio de Janeiro · | 4 upvotes · 49.1K views

Hi, community, I'm planning to build a web service that will perform a text search in a data set off less than 3k well-structured JSON objects containing config data. I'm expecting no more than 20 MB of data. The general traits I need for this search are: - Typo tolerant (fuzzy query), so it has to match the entries even though the query does not match 100% with a word on that JSON - Allow a strict match mode - Perform the search through all the JSON values (it can reach 6 nesting levels) - Ignore all Keys of the JSON; I'm interested only in the values.

The only thing I'm researching at the moment is Elasticsearch, and since the rest of the stack is on AWS the Amazon ElasticSearch is my favorite candidate so far. Although, the only knowledge I have on it was fetched from some articles and Q&A that I read here and there. Is ElasticSearch a good path for this project? I'm also considering Amazon DynamoDB (which I also don't know of), but it does not look to cover the requirements of fuzzy-search and ignore the JSON properties. Thank you in advance for your precious advice!

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Replies (3)
Roel van den Brand
Lead Developer at Di-Vision Consultion · | 3 upvotes · 38.9K views
Recommends
on
Amazon AthenaAmazon Athena

Maybe you can do it with storing on S3, and query via Amazon Athena en AWS Glue. Don't know about the performance though. Fuzzy search could otherwise be done with storing a soundex value of the fields you want to search on in a MongoDB. In DynamoDB you would need indexes on every searchable field if you want it to be efficient.

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Ted Elliott

I think elasticsearch should be a great fit for that use case. Using the AWS version will make your life easier. With such a small dataset you may also be able to use an in process library for searching and possibly remove the overhead of using a database. I don’t if it fits the bill, but you may also want to look into lucene.

I can tell you that Dynamo DB is definitely not a good fit for your use case. There is no fuzzy matching feature and you would need to have an index for each field you want to search or convert your data into a more searchable format for storing in Dynamo, which is something a full text search tool like elasticsearch is going to do for you.

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Julien DeFrance
Principal Software Engineer at Tophatter · | 3 upvotes · 37.6K views

The Amazon Elastic Search service will certainly help you do most of the heavy lifting and you won't have to maintain any of the underlying infrastructure. However, elastic search isn't trivial in nature. Typically, this will mean several days worth of work.

Over time and projects, I've over the years leveraged another solution called Algolia Search. Algolia is a fully managed, search as a service solution, which also has SDKs available for most common languages, will answer your fuzzy search requirements, and also cut down implementation and maintenance costs significantly. You should be able to get a solution up and running within a couple of minutes to an hour.

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Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 375.6K views
Needs advice
on
AlgoliaAlgoliaElasticsearchElasticsearch
and
FirebaseFirebase

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 8 upvotes · 279.9K views
Recommends
on
AlgoliaAlgolia

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Recommends
on
Cloud FirestoreCloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Pros of Elasticsearch
Pros of Sphinx
  • 328
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
  • 98
    Free
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
    Potato
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Fast
  • 2
    Easy setup
  • 1
    Open
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Not stable
  • 1
    Scalability
  • 0
    Community
  • 16
    Fast
  • 9
    Simple deployment
  • 6
    Open source
  • 1
    Lots of extentions

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Cons of Elasticsearch
Cons of Sphinx
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
    Be the first to leave a con

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    What is Elasticsearch?

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

    What is Sphinx?

    It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.

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    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

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    GitHubPythonReact+42
    49
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    GitHubPythonNode.js+47
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    What are some alternatives to Elasticsearch and Sphinx?
    Datadog
    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
    Solr
    Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
    Lucene
    Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Algolia
    Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.
    See all alternatives